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kooff11/
13892c31-c2cd-4d50-8fd5-94eeb70fd4c8

Name: 13892c31-c2cd-4d50-8fd5-94eeb70fd4c8
Availability: InStock

generated_from_trainer

JackFram/llama-68m

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - b83f6f5cc8eb865d_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/b83f6f5cc8eb865d_train_data.json
  type:
    field_input: Opening Text
    field_instruction: Headline
    field_output: HTML source
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: false
group_by_length: false
hub_model_id: kooff11/13892c31-c2cd-4d50-8fd5-94eeb70fd4c8
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
  0: 130GiB
  1: 130GiB
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/b83f6f5cc8eb865d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
quantization_config:
  llm_int8_enable_fp32_cpu_offload: false
  load_in_8bit: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 4056
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 13892c31-c2cd-4d50-8fd5-94eeb70fd4c8
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 13892c31-c2cd-4d50-8fd5-94eeb70fd4c8
warmup_steps: 2
weight_decay: 0.0
xformers_attention: null

13892c31-c2cd-4d50-8fd5-94eeb70fd4c8

This model is a fine-tuned version of JackFram/llama-68m on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.3888

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 32
total_train_batch_size: 128
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 2
training_steps: 36

Training results

Training Loss	Epoch	Step	Validation Loss
5.1122	0.0279	1	4.7187
4.7503	0.2509	9	4.5686
4.6001	0.5017	18	4.4613
4.5914	0.7526	27	4.3993
6.8891	1.0139	36	4.3888

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1